Mass Spectrometry Provides a Highly Sensitive Noninvasive Means of Sequencing and Tracking M-Protein in the Blood of Multiple Myeloma Patients

peer-reviewed · Journal of Proteome Research · 2021

peer-reviewed · Journal of Proteome Research · 2021. Zac McDonald et al. The amino acid sequence of the M-protein for multiple myeloma is unique compared to the polyclonal antibodies…
Date 2021-08-06
Type peer-reviewed
Venue Journal of Proteome Research
Publisher American Chemical Society (ACS)
Contribution downstream-application
DOI 10.1021/acs.jproteome.0c01022
Citations (OpenAlex) 22
Venue 2-year citedness 3.83

Abstract

The amino acid sequence of the M-protein for multiple myeloma is unique compared to the polyclonal antibodies in patients’ blood. This uniqueness is exploited to develop an ultrasensitive M-protein detection method utilizing mass spectrometry (MS). The method involves the de novo amino acid sequencing of the full-length M-protein, and a targeted MS/MS assay to detect and quantify the unique M-protein sequence in serum samples. Healthy control serum spiked with NISTmAb and serial samples from an MM patient were used to demonstrate the ability of the platform to sequence and monitor a target M-protein. The de novo NISTmAb protein sequence obtained matched the published sequence, confirming the ability of the platform to accurately sequence a target M-protein in serum. NISTmAb was quantified down to 0.0002 g/dL in serum, a level hundreds of times more sensitive than conventional blood-based tests such as SPEP and IFE. The M-protein in the patient sample could be quantified throughout complete remission, demonstrating the utility of the assay to track M-protein considerably beyond the sensitivities of current blood-based tests. Notably, the assay detected a 2-fold rise in M-protein levels 10 months before any changes were detected by conventional IFE. The MS-based assay is highly sensitive, noninvasive, and requires only a small amount of serum, less than 100 μL. Sequencing data is deposited into PRIDE with identifier PXD022784, and quantification data can be found in Panorama Public with identifier PXD022980.

Authors

  1. Zac McDonald
  2. Paul Taylor
  3. Mariya Liyasova
  4. Qixin Liu · Rapid Novor Inc.
  5. Bin Ma · Rapid Novor Inc., University of Waterloo, University of Western Ontario, Western University

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